Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Reģresijas kvantiļu uz kvantiļu panelī× | Grindera koeficientālās pārbaudes panelim× | |
|---|---|---|
| Nozare | Ekonometrija | Ekonometrija |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 2015 (QQ); panel applications from ~2018 | 1988–2012 |
| Autors≠ | Sim and Zhou (cross-section QQ); panel extension in applied energy/finance econometrics | Holtz-Eakin, Newey & Rosen (1988); Dumitrescu & Hurlin (2012) |
| Tips≠ | Nonparametric quantile regression | Causality test |
| Pirmavots≠ | Sim, N., & Zhou, H. (2015). Oil prices, US stock return, and the dependence between their quantiles. Journal of Banking and Finance, 55, 1-8. DOI ↗ | Dumitrescu, E.-I., & Hurlin, C. (2012). Testing for Granger non-causality in heterogeneous panels. Economic Modelling, 29(4), 1450–1460. DOI ↗ |
| Citi nosaukumi | Panel QQ regression, panel QQ approach, panel quantile-on-quantile approach, PQQ regression | panel causality test, Dumitrescu-Hurlin test, heterogeneous panel causality, panel Granger test |
| Saistītās≠ | 6 | 5 |
| Kopsavilkums≠ | Panel quantile-on-quantile (QQ) regression jointly maps any quantile of the outcome distribution onto any quantile of the predictor distribution across multiple cross-sectional units observed over time. It generalises Sim and Zhou's (2015) cross-sectional QQ framework to a panel setting, revealing a full dependence surface rather than a single average effect, while accounting for individual heterogeneity through fixed or random effects correction. | The Panel Granger Causality test examines whether past values of one variable help predict another variable across multiple cross-sectional units observed over time. It extends the classical Granger causality framework to panel data, accounting for cross-sectional heterogeneity and enabling more powerful inference by pooling information across units. |
| ScholarGateDatu kopa ↗ |
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